Image information, be it color, black or white, is commonly generated in a bitmap format where the bitmap comprises a plurality of gray level pixels, i.e. pixels that are defined by digital values, each value representing a gray level among a number of gray levels. Thus, in an 8 bit system, 256 levels of gray are present, where each level represents an increment of gray between black and white. In the case of color bitmaps, where three defining colors or separations each include 256 levels of information, there may be more than 16 million colors defined by a gray bitmap.
Usually, bitmaps in such a gray level format are unprintable by standard printers. Standard printers print in a limited number of levels, either a spot or a no spot in the binary case, or a limited number of levels associated with the spot, for example, four in the quaternary case. Accordingly, it is necessary to reduce or quantize the gray level image data to a limited number of levels so that it is printed. Besides gray level information derived by scanning, certain processing techniques such as those described, for example, in U.S. Pat. No. 5,226,094 to Eschbach may produce gray level data requiring quantization for printing.
One standard method of quantizing gray level pixel values to binary level pixel values is through the use of dithering or halftoning processes. In such arrangements, over a given area having a number of gray pixels therein, each pixel value of an array of gray level pixels within the area is compared to one of a set of preselected thresholds (the thresholds are stored as a dither matrix and the repetitive pattern generated by this matrix is considered a halftone cell) as taught, for example, in U.S. Pat. No. 4,149,194 to Holladay. The effect of such an arrangement is that, for an area where the image is gray, some of the thresholds within the dither matrix will be exceeded, i.e. the image value at that specific location is larger than the value stored in the dither matrix for that same location, while others are not. In the binary case, the pixels or cell elements for which the thresholds are exceeded might be printed as black, while the remaining elements are allowed to remain white, dependent on the actual physical quantity described by the data. The effect of the distribution of black and white over the halftone cell is integrated by the human eye as gray. Dithering or screening presents problems, however, in that the amount of gray within an original image is not maintained exactly over an area, because the finite number of elements inside each dither matrix - and therefore halftone cell - only allows the reproduction of a finite number of gray levels, i.e. number of elements in the cell plus one, or less. The error arising from the difference between the output pixel value and the actual gray level pixel value at any particular cell is simply thrown away.
Algorithms that convert gray images to binary or other number of level images attempting to preserve the local density exist, and include among them error diffusion, as taught, for example, in "An Adaptive Algorithm for Spatial Greyscale" by Floyd and Steinberg, Proceedings of the SID 17/2, 75-77 (1976) (hereinafter, "Floyd and Steinberg"). Error diffusion attempts to maintain image density by making the conversion from gray pixels to binary or other level pixels on a pixel-by-pixel basis. The procedure examines each pixel with respect to a threshold, and the difference between the gray level pixel value and the output value is forwarded to a selected group or set of neighboring pixels, in accordance with a weighting scheme. Another, more elaborate method would be the error diffusion techniques of U.S. Pat. No. 5,045,952 to Eschbach, which serves to provide image dependent edge enhancement. Additional modifications to the error diffusion algorithm taught by Floyd and Steinberg have been proposed, e.g.: a different weighting matrix, as taught, for example, in "A Survey of Techniques for the Display of Continuous Tone Pictures on Bilevel Displays" by Jarvis et al., Computer Graphics and Image Processing, Vol. 5., pp. 13-40 (1976) (hereinafter, Jarvis), and in "MECCA--A Multiple-Error Correction Computation Algorithm for Bi-Level Image Hardcopy Reproduction" by Stucki, IBM Res. Rep. RZ1060 (1981) (hereinafter, Stucki). Modifications of the error calculation and weight allocation have been taught, for example, in U.S. patent application Ser. No. 07/672,987 (with a counterpart application published in Japan at JP-A1 4-328957), entitled "Method for Image Conversion with Application of Multiple Error Diffusion Matrices", by Eschbach, U.S. Pat. No. 4,924,322 to Kurosawa et. al., U.S. Pat. No. 4,339,774 to Temple, U.S. Pat. No. 4,955,065, to Ulichney and U.S. patent application Ser. No. 08/167,758 by Shiau et al.
U.S. Pat. No. 5,055,943 to Levien suggests a pixel based error diffusion scheme where the tendency of the individual dots to form clusters in a screened image can be varied by applying a hysteresis constant and recursion techniques known from adaptive screening, to allow adjustment of image coarseness by adjustment of the hysteresis constant. This method produces images that reproduce easier on some output devices, particularly for electrophotographic output devices, than the original error diffusion algorithm, but the images tend to have reduced sharpness or detail resolution, as compared to Floyd and Steinberg. In implementation, the Levien method uses an error diffusion process, providing a feedback response based on the output image, and particularly, dot size. However, the hysteresis function implemented to control the feedback response tends to dampen the response at edges. The present inventor described a similar arrangement improving sharpness response using both input and output feedback loops, at U.S. Pat. No. 5,325,211 to Eschbach.
Error diffusion suffers from an important drawback in the high and low intensity image regions. In these area, the pulse or printed spot distribution generated by error diffusion becomes very non-homogeneous, and artifacts (called worms) are formed. This problem is emphasized in office documents, where shadings and backgrounds often contain large homogeneous regions of constant low/high intensity, and error diffusion artifacts become unacceptable. For example, it is common in office documents to have large areas of light colors as background for dark text and vice versa. These areas will be characterized by worming.
Several approaches have been taken to reduce the error diffusion artifacts in the low and high input intensity regions. A common approach is to increase the size of the error distribution matrix as can be found in "Digital Halftoning", by Robert Ulichney, MIT Press 1987, pp. 241, and "Reduction of Artifacts in Error Diffusion Using Input Dependent Weights, by R. Eschbach, Journal of Electronic Imaging, 2, (1993) 352-358.
All of the references cited herein are incorporated by reference for their teachings.